Comparative analysis of autoregressive models for linear prediction of ultrasonic speech

Ultrasonic speech is a novel research area with significant applications: as a speech-aid prosthesis for patients with voice box difficulties, silent speech interfaces, secure mode of communication in mobile phones and as a communication medium in high noise industrial environments. Feature extraction is a critical part of the ultrasonic speech system. Linear prediction analysis (LPA) has been recently proven to be viable for extracting features from the three dimensional ultrasonic propagation in the vocal tract (VT). A one-dimensional autoregressive model based on averaging the LP coefficients, analysed in different recording positions has been investigated by the authors to fit the LF ultrasonic resonances of the VT. To reach a state of maturity for the LPA of ultrasonic speech and in continuum of the previous work, this paper compares the application of two major conventional methods of averaging and least squares error already applied in room acoustics for deriving the coefficients in autoregressive modelling of ultrasonic speech.